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Now showing 1 - 10 of 24
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    The use of Virtual Reality in preserving and reactivating immersive audio art installations: the case of Dissonanze Circolari by Roberto Taroni
    (The Eurographics Association, 2024) Russo, Alessandro; Fayyaz, Nikoo; Franceschini, Andrea; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    Interactive multimedia artworks pose unique challenges for their preservation, such as the obsolescence of original components, software, and playback devices, and other issues related to their interactive and time-based nature. The Centro di Sonologia Computazionale (CSC) of the University of Padova developed the Multilevel Dynamic Preservation (MDP) model, which aims at ensuring the long-term preservation of multimedia artworks by treating them as dynamic objects. Reactivation is a fundamental step for allowing their preservation, and, among various reactivation strategies, Virtual Reality (VR) provides a unique opportunity to recreate the immersive experience while still maintaining the concept of the original artwork. The CSC started to work together with Italian artist Roberto Taroni, a central figure in the experimental scenario, who often combined music and visual arts in his works. This contribution concerns the reactivation in VR of Roberto Taroni's artwork ''Dissonanze Circolari'' from 1999. This installation featured a room with 16 speakers, each one playing a fragment of Beethoven's piano performance, Op.111, executed by different musicians, creating a dissonance-based immersive experience. The reactivation was carried out using the documentation provided by the artist and the audio samples from the original installation. The VR environment was created using the game engine Unreal Engine 5. This reactivation approach allows to maximize access to the artwork, providing new information for curators, scholars, and art enthusiasts.
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    S4A: Scalable Spectral Statistical Shape Analysis
    (The Eurographics Association, 2024) Maccarone, Francesca; Longari, Giorgio; Viganò, Giulio; Peruzzo, Denis; Maggioli, Filippo; Melzi, Simone; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    Statistical shape analysis is a crucial technique for studying deformations within collections of shapes, particularly in the field of Medical Imaging. However, the high density of meshes typically used to represent medical data poses a challenge for standard geometry processing tools due to their limited efficiency. While spectral approaches offer a promising solution by effectively handling high-frequency variations inherent in such data, their scalability is questioned by their need to solve eigendecompositions of large sparse matrices. In this paper, we introduce S4A, a novel and efficient method based on spectral geometry processing, that addresses these issues with a low computational cost. It operates in four stages: (i) establishing correspondences between each pair of shapes in the collection, (ii) defining a common latent space to encode deformations across the entire collection, (iii) computing statistical quantities to identify, highlight, and measure the most representative variations within the collection, and iv) performing information transfer from labeled data to large collections of shapes. Unlike previous methods, S4A provides a highly efficient solution across all stages of the process.We demonstrate the advantages of our approach by comparing its accuracy and computational efficiency to existing pipelines, and by showcasing the comprehensive statistical insights that can be derived from applying our method to a collection of medical data.
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    To What Extent Are Existing Volume Mapping Algorithms Practically Useful?
    (The Eurographics Association, 2024) Meloni, Federico; Cherchi, Gianmarco; Scateni, Riccardo; Livesu, Marco; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    Mappings between geometric domains play a crucial role in many algorithms in geometry processing and are heavily used in various applications. Despite the significant progress made in recent years, the challenge of reliably mapping two volumes still needs to be solved to an extent that is satisfactory for practical applications. This paper offers a review of provably robust volume mapping algorithms, evaluating their performances in terms of time, memory and ability to generate a correct result both with exact and inexact numerical models. We have chosen and evaluated the two most advanced methods currently available, using a state-of-the-art benchmark designed specifically for this type of analysis. We are sharing both the statistical results and specific volume mappings with the community, which can be utilized by future algorithms for direct comparative analysis. We also provide utilities for reading, writing, and validating volume maps encoded with exact rational coordinates, which is the natural form of output for robust algorithms in this class. All in all, this benchmark offers a neat overview of where do we stand in terms of ability to reliably solve the volume mapping problem, also providing practical data and tools that enable the community to compare future algorithmic developments without the need to re-run existing methods.
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    A Mixed Reality Application for Multi-Floor Building Evacuation Drills using Real-Time Pathfinding and Dynamic 3D Modeling
    (The Eurographics Association, 2024) Manfredi, Gilda; Capece, Nicola; Carlo, Rosario Pio Di; Erra, Ugo; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    In modern high-rise buildings, complex layouts and frequent structural changes often hinder emergency evacuation. Traditional evacuation plans, usually 2D diagrams, do not provide real-time guidance and are difficult for occupants to interpret. We propose a Mixed Reality (MR) application to address these challenges in real-time evacuation in multi-floor buildings. This application was developed on Meta Quest 3, chosen for its status as one of the best low-cost eXtended Reality (XR) headsets and a popular standalone Head-Mounted Display (HMD). Our system allows users to rapidly rescan and update building models, ensuring that evacuation guidance is always up-to-date. The proposed approach overcomes the Meta Quest 3 API's limitation of scanning only 15 rooms. It extends its capability by saving room data externally and using spatial anchors to maintain accurate alignment with the physical environment. Additionally, the application integrates Dijkstra's algorithm to dynamically calculate optimal escape routes based on the user's real-time location. A preliminary evaluation study demonstrates the application's effectiveness in enhancing situational awareness and enabling users to stay mentally sharp, highlighting its potential to improve decision-making and emergency response in dynamic building environments significantly.
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    Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence
    (The Eurographics Association, 2024) Riva, Alessandro; Raganato, Alessandro; Melzi, Simone; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    Current data-driven methodologies for point cloud matching demand extensive training time and computational resources, presenting significant challenges for model deployment and application. In the point cloud matching task, recent advancements with an encoder-only Transformer architecture have revealed the emergence of semantically meaningful patterns in the attention heads, particularly resembling Gaussian functions centered on each point of the input shape. In this work, we further investigate this phenomenon by integrating these patterns as fixed attention weights within the attention heads of the Transformer architecture. We evaluate two variants: one utilizing predetermined variance values for the Gaussians, and another where the variance values are treated as learnable parameters. Additionally we analyze the performances on noisy data and explore a possible way to improve robustness to noise. Our findings demonstrate that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization. Furthermore, we conducted an ablation study to identify the specific layers where the infused information is most impactful and to understand the reliance of the network on this information.
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    Persistent Homology vs. Learning Methods: A Comparative Study in Limited Data Scenarios
    (The Eurographics Association, 2024) Di Via, Andrea; Di Via, Roberto; Fugacci, Ulderico; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    This exploratory study compares persistent homology methods with traditional machine learning and deep learning techniques for label-efficient classification. We propose pure topological approaches, including persistence thresholding and Bottleneck distance classification, and explore hybrid methods combining persistent homology with machine learning. These are evaluated against conventional machine learning algorithms and deep neural networks on two binary classification tasks: surface crack detection and malaria cell identification. We assess performance across various number of samples per class, ranging from 1 to 500. Our study highlights the efficacy of persistent homology-based methods in low-data scenarios. Using the Bottleneck distance approach, we achieve 95.95% accuracy in crack detection and 93.11% in malaria diagnosis with only one labeled sample per class. These results outperform the best performance from machine learning models, which achieves 69.40% and 39.75% accuracy, respectively, and deep learning models, which attains up to 95.96% in crack detection and 62.72% in malaria diagnosis. This demonstrates the superior performance of topological methods in classification tasks with few labeled data. Hybrid approaches demonstrate enhanced performance as the number of labeled samples increases, effectively leveraging topological features to boost classification accuracy. This study highlights the robustness of topological methods in extracting meaningful features from limited data, offering promising directions for efficient, label-conserving classification strategies. The results underscore the worth of persistent homology, both as a standalone tool and in combination with machine learning, particularly in domains where labeled data scarcity challenges traditional deep learning approaches.
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    Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference: Frontmatter
    (The Eurographics Association, 2024) Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
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    Surface Reconstruction from Silhouette and Laser Scanners as a Positive-Unlabeled Learning Problem
    (The Eurographics Association, 2024) Gottardo, Mario; Pistellato, Mara; Bergamasco, Filippo; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    Typical 3D reconstruction pipelines employ a combination of line-laser scanners and robotic actuators to produce a point cloud and then proceed with surface reconstruction. In this work we propose a new technique to learn an Implicit Neural Representation (INR) of a 3D shape S without directly observing points on its surface. We just assume being able to determine whether a 3D point is exterior to S (e.g. observing if the projection falls outside the silhouette or detecting on which side of the laser line the point is). In this setting, we cast the reconstruction process as a Positive-Unlabelled learning problem where sparse 3D points, sampled according to a distribution depending on the INR's local gradient, have to be classified as being interior or exterior to S. These points, are used to train the INR in an iterative way so that its zero-crossing converges to the boundary of the shape. Preliminary experiments performed on a synthetic dataset demonstrates the advantages of the approach.
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    Mesh Comparison Using Regular Grids
    (The Eurographics Association, 2024) Kaye, Patrizia; Ivrissimtzis, Ioannis; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    A symmetric grid-based approach to mesh comparison is proposed, providing intuitive visual results alongside an objective measure of the local differences between meshes. The difference function is defined on the nodes of a regular 3D lattice, making it suitable as input for a variety of analysis algorithms. The visual results are compared and comparable to the Metro tool.
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    TACO: a Benchmark for Connectivity-invariance in Shape Correspondence
    (The Eurographics Association, 2024) Pedico, Simone; Melzi, Simone; Maggioli, Filippo; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    In real-world scenarios, a major limitation for shape-matching datasets is represented by having all the meshes of the same subject share their connectivity across different poses. Specifically, similar connectivities could provide a significant bias for shape matching algorithms, simplifying the matching process and potentially leading to correspondences based on the recurring triangle patterns rather than geometric correspondences between mesh parts. As a consequence, the resulting correspondence may be meaningless, and the evaluation of the algorithm may be misled. To overcome this limitation, we introduce TACO, a new dataset where meshes representing the same subject in different poses do not share the same connectivity, and we compute new ground truth correspondences between shapes. We extensively evaluate our dataset to ensure that ground truth isometries are properly preserved. We also use our dataset for validating state-of-the-art shape-matching algorithms, verifying a degradation in performance when the connectivity gets altered.